‘You just have to be smart’: spatial practices and subjectivity among women in sex work in London, Ontario
Bibliographic record
Abstract
Social science research on the relationship between space and sex work, specifically among women in street-based settings, demonstrates the spatialized nature of risk and how different forms of civic and legal governance contribute to their socio-economic marginalization. However, these studies rarely consider the women’s spatial practices and gendered subjectivities beyond the sex trade, which is problematic because sex work is not their singular life activity or the only impetus for their spatial movements through the urban landscape. Using social mapping and interview data from 33 women in sex work in London, Ontario, this article explores how our participants navigate the spaces where they work and live alongside those regarding health care, social services, violence and places they avoid. Findings reveal that the women traverse diverse spaces as they access health services, especially for crisis issues that necessitate travel to hospitals located beyond the inner city. The spaces used to access social services and those they avoid (i.e. to not be emotionally triggered or under police surveillance) overlap significantly, which presents unique challenges for our participants who depend upon these services for their socio-economic survival. The theoretical contributions these data make to the feminist geography literature on gender and space are discussed, particularly with respect to the issues of nomadic subjectivity and the relationality between city spaces and marginalized bodies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".